Prediction of Cyclosporine Blood Levels in Heart Transplantation Patients Using a Pharmacokinetic Model Identified by Evolutionary Algorithms

Hoda, M. Raschid; Grimm, Michael; Laufer, Guenther

doi:10.1016/j.healun.2005.02.021

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…Similar to the convenience of ChatGPT, clinicians can stratify patients confidently using complex artificial intelligence [73]. Certain studies have also developed models to predict cyclosporin and tacrolimus levels, while incorporating medication history, and the patient's individual hepatic and renal function status, to guide medical therapy in this subset of patients [74,75].…”

Section: Advanced Heart Failure and Circulatory Supportmentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

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Artificial intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this review, we evaluate the myriad applications of artificial intelligence in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of artificial intelligence such as ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of artificial intelligence as an information retrieval tool and personalized learning resource for medical students and professionals.

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Section: Advanced Heart Failure and Circulatory Supportmentioning

confidence: 99%

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

Rao,

Iqbal,

Isath

et al. 2024

Hearts

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“…AI can offer a non-invasive model to predict immunosuppressive drug blood levels. One of the earliest AI-based studies in transplantation utilized evolutionary algorithms (EAs), symbolic AI, to create a pharmacokinetic model for predicting cyclosporine blood levels in 101 heart transplant patients ( 35 ). The EA-based software tool showed accurate predictions and a strong correlation with observed levels.…”

Section: Ai and Immunosuppression In Transplantationmentioning

confidence: 99%

The transformative potential of artificial intelligence in solid organ transplantation

Al Moussawy,

Lakkis,

Ansari

et al. 2024

Front. Transplant.

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Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

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“…As for medical therapy guidance, two studies developed models to predict cyclosporin and tacrolimus levels (26,27). Both models used medication history, hepatic and renal functions, infectious status and risk factors, and patient demographics.…”

Section: Post Heart Transplant Management Guidancementioning

confidence: 99%

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

Al‐Ani

Chen

Hashky

et al. 2023

Front. Cardiovasc. Med.

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IntroductionArtificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence.MethodsWe searched Embase, Web of Science, and PubMed databases for articles containing “artificial intelligence,” “machine learning,” or “deep learning” and any of the phrases “heart transplantation,” “ventricular assist device,” or “cardiogenic shock” from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines.ResultsOf 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities.ConclusionOur review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.

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Prediction of Cyclosporine Blood Levels in Heart Transplantation Patients Using a Pharmacokinetic Model Identified by Evolutionary Algorithms

Cited by 5 publications

References 24 publications

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

An Update on the Use of Artificial Intelligence in Cardiovascular Medicine

The transformative potential of artificial intelligence in solid organ transplantation

Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review

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